Emotion recognition and prediction method and system based on multiple modes

Through the use of multimodal data acquisition and combination model, the problem of insufficient accuracy of emotion recognition and prediction in the prior art is solved, more efficient and reliable emotion recognition and prediction is achieved, and explanation of the causes of emotion formation is provided.

CN119655757AActive Publication Date: 2025-03-21ZHONGHUI ZHIXIN (CHONGQING) TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411529407.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-30
Publication Date
2025-03-21
Estimated Expiration
2044-10-30

AI Technical Summary

Technical Problem

In the prior art, emotion recognition and prediction methods mainly rely on single modal data, resulting in limited identification accuracy.

Method used

Multimodal data collection, including text data, vibration images and physiological signals, emotion recognition and prediction are performed through combined models such as deep convolutional neural network, long and short memory neural network, Transformer network and deep symbol neural network, and reverse reasoning analysis is carried out to explain the causes of emotion formation.

Benefits of technology

It improves the accuracy and reliability of emotion recognition and prediction, can more accurately identify and predict human emotions, and explain the causes of emotion formation, which enhances the interpretability and application value of the model.

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Abstract

The embodiment of the invention discloses an emotion recognition and prediction method and system based on multiple modes. The method comprises the following steps: collecting a multi-modal signal of a human individual as input data; inputting the multi-modal signal into a pre-trained and analyzed emotion recognition and prediction model for recognition and prediction to obtain a result; and reverse reasoning analysis and explanation are carried out on the result, inducements for forming emotions are found, and emotion intervention is carried out in time, so that the physical and psychological health is improved. Compared with the prior art, the precision of emotion recognition and prediction results can be further improved, more importantly, explanatable intelligent reasoning learning can be performed by combining multi-modal data of human individuals so as to explain the emotion generation mechanism therein, the reliability and credibility of the emotion recognition and prediction results are improved, and the emotion recognition and prediction accuracy is improved. And obstacles of the intelligent method in practical application are removed.
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